Abstract

Research over several decades has demonstrated modest and moderately consistent relationships between personality and music preference, but has primarily relied on self-reported data and lab-based listening experiments. Recently, music preference research has begun to take advantage of online listening data from platforms such as Spotify and Last.fm to more directly link listening behavior to individual differences. This study extends this new line of research by investigating the associations between naturally occurring music listening behaviors and personality traits, utilizing listening data acquired from Last.fm. We examined social-tagging data by extracting tags related to musical genre and emotion from frequently listened tracks of each participant and clustered them into broader categories to study their association with OCEAN traits. We further evaluated preferences in terms of co-occurring genre and emotion tags and analyzed listening patterns over time to better understand natural patterns of listening as they relate to personality. Our results corroborated previous research, demonstrating a positive correlation between Extraversion and high-arousal music genres such as hip-hop and rap, while Openness was linked with jazz sub-genres. Several novel and nuanced associations were unveiled, such as Extraversion being associated with nostalgic hip-hop and jazz, and Neuroticism correlating with calming music from unconventional genres like shoegaze. Finally, we comment on existing music preference frameworks and their relevance to naturally occurring music listening behavior.

Abstract

Lyrics play a crucial role in affecting and reinforcing emotional states by providing meaning and emotional connotations that interact with the acoustic properties of the music. Specific lyrical themes and emotions may intensify existing negative states in listeners and may lead to undesirable outcomes, especially in listeners with mood disorders such as depression. Hence, it is important for such individuals to be mindful of their listening strategies. In this study, we examine online music consumption of individuals at risk of depression in light of lyrical themes and emotions. Lyrics obtained from the listening histories of 541 Last.fm users, divided into At-Risk and No-Risk based on their mental well-being scores, were analyzed using natural language processing techniques. Statistical analyses of the results revealed that individuals at risk for depression prefer songs with lyrics associated with low valence and low arousal. Additionally, lyrics associated with themes of denial, self-reference, and ambivalence were preferred. In contrast, themes such as liberation, familiarity, and activity are not as favored. This study opens up the possibility of an approach to assessing depression risk from the digital footprint of individuals and potentially developing personalized recommendation systems.

Abstract

Autism Spectrum Disorder (ASD) presents challenges in communication, social interaction, and sensory processing. This pilot study introduces a GOAL driven Recommendation Framework for personalized music interventions, leveraging music's therapeutic functions to address emotional, cognitive, and social needs in individuals with ASD was tested on an Indian population. Through participants music playlist compilation and data collected from online sources, a diverse dataset is curated. The recommendation system employs similarity metric to recommend music aligned with specific therapeutic goals. Evaluation metrics and behavioural changes indicate significant improvements in mood management, sleep regularization, focus, and sensory regulation. However, enhancements in communication and social skills are less conclusive. This study underscores the feasibility and effectiveness of personalized music recommendations in addressing therapeutic goals for individuals with ASD, emphasizing the need for further research to explore longitudinal outcomes to assess its impact on the quality of life of individuals with ASD.

Abstract

The Patent Office. Government of India Dance Teaching Tool Design No.: 413513-001 Date: 12/04/2024 Certified that the design of which a copy is annexed hereto has been registered as of the number and date given above in class 19-07 in respect of the application of such design to DANCE TEACHING TOOL in the name of 1. International Institute of Information Technology 2. Prof. Vinoo Alluri 3. S Jayachandran In pursuance of and subject to the provisions of the Designs Act, 2000 and the Designs Rule, 2001. Date of Issue: 10/07/2024

Abstract

How we move and interact with our surroundings can reveal a lot about us as an individual. This study delves into the interplay of music, movement, and individual identity within the framework of embodied cognition. Drawing inspiration from Carlson et al. (2020)’s work, which showcased remarkably high accuracy in identifying individuals based on free-form dance movements in a marker-based setting, our investigation extends their work into two novel contexts: markerless-choreographic and marker-based-dyadic dance settings. In the choreographic setting, professional dancers perform identical routines in a markerless setting. In the dyadic setting, individuals danced with a partner. We found that the dancer identification accuracy was at least two times better than the chance level in the choreographic setting and notably high accuracy in the dyadic setting. These results showcase the robustness of Carlson et al. (2020)’s method in generalizing to new settings and the presence of motoric fingerprints in choreographic as well as dyadic settings.

Abstract

Cocaine use disorder (CUD) is described as a compulsive urge to seek and consume cocaine despite the inimical consequences. MRI studies from different modalities have shown that CUD patients exhibit structural and/or functional connectivity pathology among several brain regions. Nevertheless, both connectivities are commonly studied and analyzed separately, which may potentially obscure its relationship between them, and with the clinical pathology. Here, we compare and contrast structural and functional brain networks in CUD patients and healthy controls (HC) using multimodal fusion. The sample consisted of 63 (8 females) CUD patients and 42 (9 females) healthy controls (HC), recruited as part of the SUDMEX CONN database. For this, we computed a battery of graph-based measures from multi-shell diffusion-weighted imaging and resting state fc-fMRI to quantify local and global connectivity. Then we used multimodal canonical component analysis plus joint independent component analysis (mCCA+jICA) to compare between techniques, and evaluate group differences and its association with clinical alteration. Unimodal results showed a striatal decrease in the participation coefficient, but applied supervised data fusion revealed other regions with cocaine-related alterations in joint functional communication. When performing multimodal fusion analysis, we observed a higher centrality of the interrelationship and a lower participation coefficient in patients with CUD. In contrast to the unimodal approach, the multimodal fusion method was able to reveal latent information about brain regions involved in impairment due to cocaine abuse. The present results could help in understanding the pathology of CUD in order to develop better pre-treatment/post-treatment intervention designs

Abstract

No Results Found

Abstract

Background The Empathizing-Systemizing theory proposes that individuals with Autism Spectrum Disorder are systemizers and prefer Intense music characterized as loud , distorted, electric and percussive musical attributes and aggressive emotional attributes, typical of genres such as heavy metal, hard rock. However, there have been no studies that have tested this theory in a cross cultural setting. Aim The aim of this cross-cultural study is to gather music preferences by Indians and Non-Indians (US and Europe) and verify Baron-Cohen’s hypothesis. Method The Indian population of 20 participants (n=20; male, aged - 18-24 years), were contacted directly while for the Non-Indian population of 30 participants (n=25; US, n=5; Europe, male, aged 18-24) , we used social media platform Reddit (r/asperges and r/autism sub-reddit groups). Semi structured interviews were conducted to gather their demographic information, music they liked and disliked and how listening to music helps them in everyday life. Subsequently a mix of 10 songs each were curated comprising 5 songs they liked and out of the remaining 5 songs , 3 are of the genre they liked and 2 of the genre they disliked, taken from other individual’s playlist in a controlled setting. While playing each song, their likert responses (Like, Neutral, Dislike) were recorded per stimulus. Music Preferences were assessed based on the MUSIC model framework. Results Out of 20 participants, 18 preferred a mix of relaxing, non-aggressive, slow, quiet and instrumental musical attributes categorized as mix of Mellow and Sophisticated factor which helped in improved sleep and social skills and disliked music with loud and rebellious musical attributes categorized as Intense factor which shows that music serves for emotion regulation towards positive emotions in Indian population. In comparison , out of the 30 participants 22 preferred music categorized as Intense factor which brought in a calming factor when stressed and disliked music categorized as a mix of Mellow and Sophisticated factors which shows that music serves to discharge negative emotions in the Non-Indian group. Implications These results provide support to the Empathizing–Systemizing Theory for Non-Indian population but not for Indian population. It also suggests that cultural differences affect emotional arousal. These findings are helpful to build a recommendation system based on an individual’s preferences that can help in arousal regulation, sensory modulation difficulties commonly found among individuals with ASD.

Abstract

Music listening is a dynamic process that entails complex interactions between sensory, cognitive, and emotional processes. The naturalistic paradigm provides a means to investigate these processes in an ecologically valid manner by allowing experimental settings that mimic real-life musical experiences. In this paper, we highlight the importance of the naturalistic paradigm in studying dynamic music processing and discuss how it allows for investigating both the segregation and integration of brain processes using model-based and model-free methods. We further suggest that studying individual difference-modulated music processing in this paradigm can provide insights into the mechanisms of brain plasticity, which can have implications for the development of interventions and therapies in a personalized way. Finally, despite the challenges that the naturalistic paradigm poses, we end with a discussion on future prospects of music and neuroscience research, especially with the continued development and refinement of naturalistic paradigms and the adoption of open science practices.

Abstract

Individual differences are known to modulate brain responses to music. Recent neuroscience research suggests that each individual has unique and fundamentally stable functional brain connections irrespective of task. Our study aims to identify individual differences such as gender and musical expertise from brain responses to music. Thirty-six participants’ functional Magnetic Resonance Imaging (fMRI) responses were measured while listening to three 8-min-long musical pieces representing different musical styles. They were analyzed using various temporal and spectral functional connectivity (FC) measures. These FC measures were compared to identify the one that captured the most variation associated with gender and musical expertise. Subsequently, the measures were used to classify distinct population groupings using a binary Support Vector Machine (SVM). Our results revealed that Coherence, a spectral-domain FC measure, captured the maximum variation for musical stimuli. However, in classifying individuals based on gender and musical expertise, a composite measure outperformed any single measure and had abovechance accuracy. This suggests that each FC measure captures different aspects of the relationship between brain regions and is influenced by the level of analysis (group or individual) and the task, such as similarity analysis or classification. Further ramifications of the findings are discussed.